Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
colbert
late-interaction
retrieval
pylate
multi-vector
text-embeddings-inference
Instructions to use chungimungi/GLInt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use chungimungi/GLInt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chungimungi/GLInt") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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| Model | Average | ArguAna | ClimateFEVER | DBPedia | FEVER | FiQA2018 | HotpotQA | MS MARCO | NFCorpus | Natural Questions | Quora | SciDocs | SciFact | TREC-COVID | Touché-2020 |
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| LateOn | 61.4 | 52.2 | 42.1 | 31.7 | 92.7 | **57.9** | 78.9 | 70.3 | 27.0 | 93.1 | 91.5 | 15.1 | 88.9 | 80.9 | 36.8 |
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| DenseOn | 58.8 | 40.0 | 39.5 | 28.8 | 91.2 | 55.9 | 73.7 | 68.9 | 28.5 | 92.1 | 91.1 | 14.7 | 85.4 | 82.5 | 31.0 |
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| pplx-embed-v1-0.6b | 59.7 | 43.7 | 42.4 | 28.4 | 91.1 | 55.2 | 73.5 | 71.9 | 28.0 | 91.6 | 91.5 | 15.4 | 89.0 | 83.7 | 30.0 |
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| Model | Average | ArguAna | ClimateFEVER | DBPedia | FEVER | FiQA2018 | HotpotQA | MS MARCO | NFCorpus | Natural Questions | Quora | SciDocs | SciFact | TREC-COVID | Touché-2020 |
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| **GLInt** | **62.50** | 51.67 | 36.35 | 42.50 | 92.89 | 56.88 | **81.16** | 72.70 | 26.21 | **94.97** | **92.06** | **22.02** | **89.07** | 81.51 | 34.97 |
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| LateOn | 61.4 | 52.2 | 42.1 | 31.7 | 92.7 | **57.9** | 78.9 | 70.3 | 27.0 | 93.1 | 91.5 | 15.1 | 88.9 | 80.9 | 36.8 |
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| DenseOn | 58.8 | 40.0 | 39.5 | 28.8 | 91.2 | 55.9 | 73.7 | 68.9 | 28.5 | 92.1 | 91.1 | 14.7 | 85.4 | 82.5 | 31.0 |
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| pplx-embed-v1-0.6b | 59.7 | 43.7 | 42.4 | 28.4 | 91.1 | 55.2 | 73.5 | 71.9 | 28.0 | 91.6 | 91.5 | 15.4 | 89.0 | 83.7 | 30.0 |
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